We adopt the standard setup of BERT for question answering [1]} and model \(P(a_i)\)  as a product of start and end distributions.
For the input, we concatenate the answer sentence
and the article as a single long sequence, separated by delimiters: [CLS] [answer sentence] [SEP] [document]. Following [2]}, we add two tokens: the start of sentence token [sos] and the sentence ID, before every sentence in the article. We train the model to predict the span of the two added tokens in front of the anchor sentence.
